arXiv subjects

Yang Li

Publications and source records attributed to Yang Li.

At least 595 records · Page 33Linked to original sources

arXiv2025

The large mass limit of $G_2$ and Calabi-Yau monopoles

We develop a structure theory for the limit of $SU(2)$ $G_2$-monopoles (resp. Calabi-Yau monopoles) on a principal $SU(2)$-bundle over an asymptotically conical $G_2$-manifolds (resp. Calabi-Yau 3-folds) as the mass parameter tends to infinity, while the topologial data for the bundle stays fixed. We show how to extract a singular abelian $G_2$-monopole (resp. Calabi-Yau monopole) with Dirac singularity along a calibrated cycle in the large mass limit, and we prove an energy identity for monopole bubbles.

math.DG↗

arXiv2025

High-rate discrete-modulated continuous-variable quantum key distribution with composable security

Continuous-variable quantum key distribution holds the potential to generate high secret key rates, making it a prime candidate for high-rate metropolitan quantum network applications. However, despite these promising opportunities, the realization of high-rate continuous-variable quantum key distribution systems with composable security remains an elusive goal. Here, we report a discrete-modulated continuous-variable quantum key distribution system with a composable secret key rate of 18.93 Mbps against collective attacks over a 25 km fiber channel. This record-breaking rate is achieved through the probability shaped 16QAM-modulated protocol, which employs semidefinite programming to ensure its composable security. Furthermore, we have employed a fully digital and precise quantum signal processing technique to reduce excess noise to extremely low levels, thereby facilitating efficient broadband system operation. While ensuring low complexity and cost, our system achieves a performance advantage of over an order of magnitude compared to previous continuous-variable quantum key distribution systems, providing a promising solution for future deployment of quantum key distribution.

quant-ph↗

arXiv2025

SSD: A State-based Stealthy Backdoor Attack For Navigation System in UAV Route Planning

Unmanned aerial vehicles (UAVs) are increasingly employed to perform high-risk tasks that require minimal human intervention. However, UAVs face escalating cybersecurity threats, particularly from GNSS spoofing attacks. While previous studies have extensively investigated the impacts of GNSS spoofing on UAVs, few have focused on its effects on specific tasks. Moreover, the influence of UAV motion states on the assessment of network security risks is often overlooked. To address these gaps, we first provide a detailed evaluation of how motion states affect the effectiveness of network attacks. We demonstrate that nonlinear motion states not only enhance the effectiveness of position spoofing in GNSS spoofing attacks but also reduce the probability of speed-related attack detection. Building upon this, we propose a state-triggered backdoor attack method (SSD) to deceive GNSS systems and assess its risk to trajectory planning tasks. Extensive validation of SSD's effectiveness and stealthiness is conducted. Experimental results show that, with appropriately tuned hyperparameters, SSD significantly increases positioning errors and the risk of task failure, while maintaining 100% stealth across three state-of-the-art detectors.

cs.CR↗

arXiv2025

Post-interactive Multimodal Trajectory Prediction for Autonomous Driving

Modeling the interactions among agents for trajectory prediction of autonomous driving has been challenging due to the inherent uncertainty in agents' behavior. The interactions involved in the predicted trajectories of agents, also called post-interactions, have rarely been considered in trajectory prediction models. To this end, we propose a coarse-to-fine Transformer for multimodal trajectory prediction, i.e., Pioformer, which explicitly extracts the post-interaction features to enhance the prediction accuracy. Specifically, we first build a Coarse Trajectory Network to generate coarse trajectories based on the observed trajectories and lane segments, in which the low-order interaction features are extracted with the graph neural networks. Next, we build a hypergraph neural network-based Trajectory Proposal Network to generate trajectory proposals, where the high-order interaction features are learned by the hypergraphs. Finally, the trajectory proposals are sent to the Proposal Refinement Network for further refinement. The observed trajectories and trajectory proposals are concatenated together as the inputs of the Proposal Refinement Network, in which the post-interaction features are learned by combining the previous interaction features and trajectory consistency features. Moreover, we propose a three-stage training scheme to facilitate the learning process. Extensive experiments on the Argoverse 1 dataset demonstrate the superiority of our method. Compared with the baseline HiVT-64, our model has reduced the prediction errors by 4.4%, 8.4%, 14.4%, 5.7% regarding metrics minADE6, minFDE6, MR6, and brier-minFDE6, respectively.

cs.CV↗

arXiv2025

HunyuanVideo: A Systematic Framework For Large Video Generative Models

Recent advancements in video generation have significantly impacted daily life for both individuals and industries. However, the leading video generation models remain closed-source, resulting in a notable performance gap between industry capabilities and those available to the public. In this report, we introduce HunyuanVideo, an innovative open-source video foundation model that demonstrates performance in video generation comparable to, or even surpassing, that of leading closed-source models. HunyuanVideo encompasses a comprehensive framework that integrates several key elements, including data curation, advanced architectural design, progressive model scaling and training, and an efficient infrastructure tailored for large-scale model training and inference. As a result, we successfully trained a video generative model with over 13 billion parameters, making it the largest among all open-source models. We conducted extensive experiments and implemented a series of targeted designs to ensure high visual quality, motion dynamics, text-video alignment, and advanced filming techniques. According to evaluations by professionals, HunyuanVideo outperforms previous state-of-the-art models, including Runway Gen-3, Luma 1.6, and three top-performing Chinese video generative models. By releasing the code for the foundation model and its applications, we aim to bridge the gap between closed-source and open-source communities. This initiative will empower individuals within the community to experiment with their ideas, fostering a more dynamic and vibrant video generation ecosystem. The code is publicly available at https://github.com/Tencent/HunyuanVideo.

cs.CV↗

arXiv2025

Dissecting a strongly coupled scalar nucleon

We continue our investigation of the stress within a strongly coupled scalar nucleon, and now dissect the gravitational form factors into contributions from its constituents, the (mock) nucleon and the (mock) pion. The computation is based on a non-perturbative solution of the scalar Yukawa model in the light-front Hamiltonian formalism with a Fock sector expansion including up to one nucleon and two pions. By employing the ``good currents" $T^{++}_i$, $T^{+-}_i$ and $T^{12}_i$, we extract the full set of gravitational form factors $A_i$, $D_i$, $\bar c_i$ without the contamination of the spurious form factors, and free of uncanceled UV divergences. With these results, we decompose the mass of the system into its constituents and compute the matter and mechanical radii, gaining insights into the strongly coupled system.

hep-ph↗

arXiv2025

UVRM: A Scalable 3D Reconstruction Model from Unposed Videos

Large Reconstruction Models (LRMs) have recently become a popular method for creating 3D foundational models. Training 3D reconstruction models with 2D visual data traditionally requires prior knowledge of camera poses for the training samples, a process that is both time-consuming and prone to errors. Consequently, 3D reconstruction training has been confined to either synthetic 3D datasets or small-scale datasets with annotated poses. In this study, we investigate the feasibility of 3D reconstruction using unposed video data of various objects. We introduce UVRM, a novel 3D reconstruction model capable of being trained and evaluated on monocular videos without requiring any information about the pose. UVRM uses a transformer network to implicitly aggregate video frames into a pose-invariant latent feature space, which is then decoded into a tri-plane 3D representation. To obviate the need for ground-truth pose annotations during training, UVRM employs a combination of the score distillation sampling (SDS) method and an analysis-by-synthesis approach, progressively synthesizing pseudo novel-views using a pre-trained diffusion model. We qualitatively and quantitatively evaluate UVRM's performance on the G-Objaverse and CO3D datasets without relying on pose information. Extensive experiments show that UVRM is capable of effectively and efficiently reconstructing a wide range of 3D objects from unposed videos.

cs.CV↗

arXiv2025

FedSem: A Resource Allocation Scheme for Federated Learning Assisted Semantic Communication

Semantic communication (SemCom), regarded as the evolution of the traditional Shannon's communication model, stresses the transmission of semantic information instead of the data itself. Federated learning (FL), owing to its distributed learning and privacy-preserving properties, has received attention from both academia and industry. In this paper, we introduce a system that integrates FL and SemCom, which is called FedSem. We have also proposed an optimization problem related to resource allocation for this system. The objective of this problem is to minimize the energy consumption and delay of FL, as well as the transmission energy of SemCom, while maximizing the accuracy of the model trained through FL. The channel access scheme is Orthogonal Frequency-Division Multiple Access (OFDMA). The optimization variables include the binary (0-1) subcarrier allocation indicator, the transmission power of each device on specific subcarriers, the computational frequency of each participating device, and the compression rate for SemCom. To tackle this complex problem, we propose a resource allocation algorithm that decomposes the original problem into more tractable subproblems. By employing convex optimization techniques, we transform the non-convex problem into convex forms, ensuring tractability and solution effectiveness. Our approach includes a detailed analysis of time complexity and convergence, proving the practicality of the algorithm. Numerical experiments validate the effectiveness of our approach, showing superior performance of our algorithm in various scenarios compared to baseline methods. Hence, our solution is useful for enhancing the operational efficiency of FedSem systems, offering significant potential for real-world applications.

cs.DC↗

arXiv2025

The day-ahead scenario generation method for new energy based on an improved conditional generative diffusion model

In the context of the rising share of new energy generation, accurately generating new energy output scenarios is crucial for day-ahead power system scheduling. Deep learning-based scenario generation methods can address this need, but their black-box nature raises concerns about interpretability. To tackle this issue, this paper introduces a method for day-ahead new energy scenario generation based on an improved conditional generative diffusion model. This method is built on the theoretical framework of Markov chains and variational inference. It first transforms historical data into pure noise through a diffusion process, then uses conditional information to guide the denoising process, ultimately generating scenarios that satisfy the conditional distribution. Additionally, the noise table is improved to a cosine form, enhancing the quality of the generated scenarios. When applied to actual wind and solar output data, the results demonstrate that this method effectively generates new energy output scenarios with good adaptability.

cs.LG↗

arXiv2025

GENET: Unleashing the Power of Side Information for Recommendation via Hypergraph Pre-training

Recommendation with side information has drawn significant research interest due to its potential to mitigate user feedback sparsity. However, existing models struggle with generalization across diverse domains and types of side information. In particular, three challenges have not been addressed, and they are (1) the diverse formats of side information, including text sequences. (2) The diverse semantics of side information that describes items and users from multi-level in a context different from recommendation systems. (3) The diverse correlations in side information to measure similarity over multiple objects beyond pairwise relations. In this paper, we introduce GENET (Generalized hypErgraph pretraiNing on sidE informaTion), which pre-trains user and item representations on feedback-irrelevant side information and fine-tunes the representations on user feedback data. GENET leverages pre-training as a means to prevent side information from overshadowing critical ID features and feedback signals. It employs a hypergraph framework to accommodate various types of diverse side information. During pre-training, GENET integrates tasks for hyperlink prediction and self-supervised contrast to capture fine-grained semantics at both local and global levels. Additionally, it introduces a unique strategy to enhance pre-training robustness by perturbing positive samples while maintaining high-order relations. Extensive experiments demonstrate that GENET exhibits strong generalization capabilities, outperforming the SOTA method by up to 38% in TOP-N recommendation and Sequential recommendation tasks on various datasets with different side information.

cs.IR↗

arXiv2025

A General Optimization Framework for Tackling Distance Constraints in Movable Antenna-Aided Systems

The recently emerged movable antenna (MA) shows great promise in leveraging spatial degrees of freedom to enhance the performance of wireless systems. However, resource allocation in MA-aided systems faces challenges due to the nonconvex and coupled constraints on antenna positions. This paper systematically reveals the challenges posed by the minimum antenna separation distance constraints. Furthermore, we propose a penalty optimization framework for resource allocation under such new constraints for MA-aided systems. Specifically, the proposed framework separates the non-convex and coupled antenna distance constraints from the movable region constraints by introducing auxiliary variables. Subsequently, the resulting problem is efficiently solved by alternating optimization, where the optimization of the original variables resembles that in conventional resource allocation problem while the optimization with respect to the auxiliary variables is achieved in closedform solutions. To illustrate the effectiveness of the proposed framework, we present three case studies: capacity maximization, latency minimization, and regularized zero-forcing precoding. Simulation results demonstrate that the proposed optimization framework consistently outperforms state-of-the-art schemes.

eess.SP↗

arXiv2025

Human-aligned Safe Reinforcement Learning for Highway On-Ramp Merging in Dense Traffic

Most reinforcement learning (RL) approaches for the decision-making of autonomous driving consider safety as a reward instead of a cost, which makes it hard to balance the tradeoff between safety and other objectives. Human risk preference has also rarely been incorporated, and the trained policy might be either conservative or aggressive for users. To this end, this study proposes a human-aligned safe RL approach for autonomous merging, in which the high-level decision problem is formulated as a constrained Markov decision process (CMDP) that incorporates users' risk preference into the safety constraints, followed by a model predictive control (MPC)-based low-level control. The safety level of RL policy can be adjusted by computing cost limits of CMDP's constraints based on risk preferences and traffic density using a fuzzy control method. To filter out unsafe or invalid actions, we design an action shielding mechanism that pre-executes RL actions using an MPC method and performs collision checks with surrounding agents. We also provide theoretical proof to validate the effectiveness of the shielding mechanism in enhancing RL's safety and sample efficiency. Simulation experiments in multiple levels of traffic densities show that our method can significantly reduce safety violations without sacrificing traffic efficiency. Furthermore, due to the use of risk preference-aware constraints in CMDP and action shielding, we can not only adjust the safety level of the final policy but also reduce safety violations during the training stage, proving a promising solution for online learning in real-world environments.

cs.RO↗

arXiv2025

GSA-Forecaster: Forecasting Graph-Based Time-Dependent Data with Graph Sequence Attention

Forecasting graph-based, time-dependent data has broad practical applications but presents challenges. Effective models must capture both spatial and temporal dependencies in the data, while also incorporating auxiliary information to enhance prediction accuracy. In this paper, we identify limitations in current state-of-the-art models regarding temporal dependency handling. To overcome this, we introduce GSA-Forecaster, a new deep learning model designed for forecasting in graph-based, time-dependent contexts. GSA-Forecaster utilizes graph sequence attention, a new attention mechanism proposed in this paper, to effectively manage temporal dependencies. GSA-Forecaster integrates the data's graph structure directly into its architecture, addressing spatial dependencies. Additionally, it incorporates auxiliary information to refine its predictions further. We validate its performance using real-world graph-based, time-dependent datasets, where it demonstrates superior effectiveness compared to existing state-of-the-art models.

cs.LG↗

arXiv2025

FBChain: A Blockchain-based Federated Learning Model with Efficiency and Secure Communication

Privacy and security in the parameter transmission process of federated learning are currently among the most prominent concerns. However, there are two thorny problems caused by unprotected communication methods: "parameter-leakage" and "inefficient-communication". This article proposes Blockchain-based Federated Learning (FBChain) model for federated learning parameter communication to overcome the above two problems. First, we utilize the immutability of blockchain to store the global model and hash value of local model parameters in case of tampering during the communication process, protect data privacy by encrypting parameters, and verify data consistency by comparing the hash values of local parameters, thus addressing the "parameter-leakage" problem. Second, the Proof of Weighted Link Speed (PoWLS) consensus algorithm comprehensively selects nodes with the higher weighted link speed to aggregate global model and package blocks, thereby solving the "inefficient-communication" problem. Experimental results demonstrate the effectiveness of our proposed FBChain model and its ability to improve model communication efficiency in federated learning.

cs.CR↗

arXiv2025

How cancer emerges: Data-driven universal insights into tumorigenesis via hallmark networks

Cancer is a complex disease driven by dynamic regulatory shifts that cannot be fully captured by individual molecular profiling. We employ a data-driven approach to construct a coarse-grained dynamic network model based on hallmark interactions, integrating stochastic differential equations with gene regulatory network data to explore key macroscopic dynamic changes in tumorigenesis. Our analysis reveals that network topology undergoes significant reconfiguration before hallmark expression shifts, serving as an early indicator of malignancy. A pan-cancer examination across $15$ cancer types uncovers universal patterns, where Tissue Invasion and Metastasis exhibits the most significant difference between normal and cancer states, while the differences in Reprogramming Energy Metabolism are the least pronounced, consistent with the characteristic features of tumor biology. These findings reinforce the systemic nature of cancer evolution, highlighting the potential of network-based systems biology methods for understanding critical transitions in tumorigenesis.

q-bio.QM↗

arXiv2025

Breaking Down Power Barriers in On-Device Streaming ASR: Insights and Solutions

Power consumption plays a crucial role in on-device streaming speech recognition, significantly influencing the user experience. This study explores how the configuration of weight parameters in speech recognition models affects their overall energy efficiency. We found that the influence of these parameters on power consumption varies depending on factors such as invocation frequency and memory allocation. Leveraging these insights, we propose design principles that enhance on-device speech recognition models by reducing power consumption with minimal impact on accuracy. Our approach, which adjusts model components based on their specific energy sensitivities, achieves up to 47% lower energy usage while preserving comparable model accuracy and improving real-time performance compared to leading methods.

cs.SD↗

arXiv2025

Resolving Rydberg-Electron Recapture Dynamics via Laser-driven Frustrated Tunneling Ionization

By employing two-color counter-rotating circularly polarized laser fields, we investigate the dynamics of electron recapture into Rydberg states under strong, ultrashort laser pulses, probed via coherent extreme-ultraviolet free-induction decay (XFID). Our study reveals significant distinctions between XFID and above-threshold high-order harmonic generation in terms of their ellipticity dependence on the driving-laser waveforms, yield variations with the laser-intensity ratios, and sensitivity to the driving-laser ellipticity. All these differences arise from the fundamentally distinct electron trajectories underlying the two processes. More importantly, our findings provide compelling evidence that Rydberg-electron recapture predominantly occurs at the end of the driving laser field, offering the first direct experimental confirmation of this long-proposed mechanism.

physics.optics↗

arXiv2025

Scalable, universal and conformal direct electrodes microprinting for high-performance van der Waals-integrated two-dimensional electronics and flexible applications

Two-dimensional (2D) materials with extraordinary electrical properties, hold promising for large-scale, flexible electronics. However, their device performance could be hindered due to the excessive defects introduced via traditional electrode integration processes. Transfer printing techniques have been developed for van der Waals contacts integration, while existing techniques encounter limitations in achieving conformal electrode transfer and compatibility with flexible devices. Here we introduce a highly conformal microprinting technique utilizing polypropylene carbonate (PPC)/Polyvinyl alcohol (PVA) copolymer, which enables successful transfer of wafer-scale, micropatterned electrodes onto diverse substrates, including those with complex geometries. This technique, implemented with 2D transition metal dichalcogenides (TMDCs), yields 2D field-effect transistors with near-ideal ohmic contacts, and a record-high carrier mobility up to 334 cm2 V-1 s-1 for a WSe2 device. Furthermore, we fabricated transistor arrays on MoS2 thin film, which show uniform device performance. We also present the flexible MoS2 transistors that not only achieve a high electron mobility of up to 111 cm2 V-1 s-1 but also exhibit outstanding mechanical robustness. Our findings represent a significant leap forward in the fabrication of flexible 2D electronics, paving the way for numerous emerging technologies.

physics.app-ph↗